We design our entropy coder based on rANS [1]}, and save computation time by precalculating intermediate results.
Previous works such as tANS [1]} already adopt this idea. However, tANS can only code a fixed distribution, which is inappropriate for AI algorithms.
On the other hand, our proposed coder is more similar to dynamic ones, as it supports distributions with variable parameters.
The only difference is that the parameters need to be quantized, and probability mass needs to be calculated for all of them. Thus we call it a semi-dynamic entropy coder.
